Knowledge catalogue
arXiv:2610.18949v1 Announce Type: cross Abstract: Existing machine-learning models for perovskite solar cells struggle to predict devices made from new materials. Most treat device layers as arbitrary
arXiv:2610.20083v1 Announce Type: new Abstract: Visual world knowledge is a core capability of multimodal large language models (MLLMs), underpinning knowledge-intensive visual question answering and
arXiv:2610.19498v1 Announce Type: cross Abstract: Model signing authenticates the full-precision weights W an LLM is released with, yet users run a quantized artifact Q whose producing transformation
arXiv:2610.17643v1 Announce Type: new Abstract: An enterprise agent that answers under a written policy must choose an action before it says anything: answer, ask for a missing fact, ask for confirmat
arXiv:2610.14334v1 Announce Type: new Abstract: In Model Context Protocol (MCP) systems, LLMs select tools from metadata rather than from their implementations. Names and descriptions summarize what a
arXiv:2610.15228v1 Announce Type: new Abstract: Words are increasingly becoming an interface for action: large language models (LLMs) can now invoke tools, operate devices, and make decisions that aff
arXiv:2610.15666v1 Announce Type: new Abstract: Converting natural-language instructions into executable action plans conditioned on a scene image is a core capability for embodied human-robot interac
arXiv:2610.18305v1 Announce Type: new Abstract: The world encompasses both physical environments and human actors. Predicting its evolution requires understanding both physical laws and how emotions s
arXiv:2610.16324v1 Announce Type: new Abstract: Training-free language-based video anomaly detection (VAD) uses frozen vision--language models (VLMs) and large language models (LLMs) to localize abnor
arXiv:2610.14151v1 Announce Type: cross Abstract: Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for r
arXiv:2610.18399v1 Announce Type: new Abstract: Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned perform
arXiv:2610.14395v1 Announce Type: new Abstract: Multimodal prediction models add a data source, such as imaging or an assay, to clinical information, and health systems must decide whether that added
arXiv:2610.17387v1 Announce Type: new Abstract: Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: s
arXiv:2609.14421v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement infor
arXiv:2610.17128v1 Announce Type: new Abstract: Egocentric video provides a continuous first-person record of everyday activities, making it a natural foundation for wearable AI assistants. Unlike off
arXiv:2610.15452v1 Announce Type: new Abstract: Text-to-video (T2V) models are increasingly used to generate videos with targeted emotional expressions, yet existing evaluation methods mainly rely on
arXiv:2610.14977v1 Announce Type: new Abstract: Embodied agents built on foundation models rely on an execution harness to organize observations, guide actions, and incorporate execution feedback into
arXiv:2511.07923v3 Announce Type: replace Abstract: Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain la
arXiv:2610.15284v1 Announce Type: new Abstract: Drug discovery requires efficient exploration of an astronomically large chemical space. Molecular graph diffusion models offer a promising approach to
arXiv:2610.17859v1 Announce Type: new Abstract: Thermal-to-visible (T2V) face translation has important applications in nighttime biometric recognition, low-light surveillance, and human interpretatio
arXiv:2610.13995v1 Announce Type: new Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to gener
arXiv:2610.15957v1 Announce Type: cross Abstract: Transformer models trained on symbolic music have become remarkably capable. However, it is yet unclear how they represent music structure, and whethe
arXiv:2610.17368v1 Announce Type: cross Abstract: Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively. We intr
arXiv:2610.18238v1 Announce Type: new Abstract: Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page
arXiv:2610.16759v1 Announce Type: new Abstract: Latent diffusion models generate images as codes of an image tokenizer, whose decoder turns them into the pixel image. Recent tokenizers improve how wel
arXiv:2610.15032v1 Announce Type: new Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which
arXiv:2610.13239v2 Announce Type: replace Abstract: Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coo
arXiv:2610.14537v1 Announce Type: cross Abstract: Particle-in-cell (PIC) simulation is indispensable for modeling beam dynamics in particle accelerators, but its computational cost limits many-query w
arXiv:2509.13334v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on th
arXiv:2610.17792v1 Announce Type: new Abstract: GUI grounding enables web agents to translate natural-language instructions into actionable interface locations. However, dense layouts and small, visua
arXiv:2610.17429v1 Announce Type: new Abstract: Training large language models is constrained by both wall-clock time and memory footprint, and recent matrix-aware optimizers sharpen this tradeoff: Mu
arXiv:2610.17656v1 Announce Type: new Abstract: Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence. Open-o3-Video enables verifiable reasoning by
arXiv:2610.18571v1 Announce Type: new Abstract: Different MRI modalities reveal different parts of a brain tumor. Segmentation models combine information from these modalities to outline tumor regions
arXiv:2404.16914v2 Announce Type: replace Abstract: MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing paramet
arXiv:2610.18054v1 Announce Type: cross Abstract: Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks
arXiv:2603.19844v3 Announce Type: replace Abstract: Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on cl
arXiv:2610.15814v1 Announce Type: new Abstract: General-purpose multimodal foundation models are increasingly capable of directly controlling robots, yet improving their manipulation capabilities thro
arXiv:2511.00655v3 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by
arXiv:2610.16349v1 Announce Type: cross Abstract: Which context tokens drive a language model's answer? Graph-based tools such as attention rollout and attention flow answer this from a single forward
arXiv:2610.17867v1 Announce Type: new Abstract: Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. Howev
arXiv:2610.14196v1 Announce Type: cross Abstract: Late-interaction (multi-vector) models represent documents using a set of token-level vectors, providing a middle ground between cross-encoders and de
arXiv:2610.18472v1 Announce Type: new Abstract: Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialize
arXiv:2610.18276v1 Announce Type: new Abstract: Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in groun
arXiv:2610.18516v1 Announce Type: new Abstract: Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and d
arXiv:2610.16070v1 Announce Type: cross Abstract: Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training mo
arXiv:2610.15961v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now
arXiv:2610.17696v1 Announce Type: new Abstract: Identifying protein-ligand binding sites (pockets) is important for understanding protein function and developing drugs. However, no public method has l
After much testing and talking to actual users across all three proactive AI agents: Muse - best one for your parents, heavy IG/FB users, people who like or need easy setups, people who don’t want to
graph: skip the LM head for pooled embeddings (#30445) graph: skip the LM head for pooled embeddings A context that extracts pooled embeddings outputs one vector per sequence, but every model still ex
So I wanted to try the horsey challenge myself on the latest engine i been testing which is a strata fork by sergqwer. https://github.com/sergqwer/strata-nvfp4. I seen a post earlier and seen i believ
I’ve been building SonicBoom, a native tensor runtime written in C++/CUDA with a C ABI and no Python anywhere in the stack. I just tagged v0.0.1, and the first target is local LLM inference on consume
arXiv:2610.13184v1 Announce Type: new Abstract: While reinforcement learning with verifiable reward (RLVR) enhances reasoning in large language models, its efficacy in long-horizon multimodal tasks re
arXiv:2610.13627v1 Announce Type: new Abstract: Evaluating large language models (LLMs) on AMLworld's smallest test splits is costly because each of over a million targets requires a separate graph pr
Write-up: https://zhebrak.io/posts/brokenml/ Repo: https://github.com/zhebrak/brokenml I built a benchmark to determine the access level agents need to successfully fix broken ML workloads (PyTorch ov
arXiv:2610.13862v1 Announce Type: new Abstract: Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory. We propose BT-
arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although
arXiv:2610.13217v1 Announce Type: cross Abstract: Video generation supports digital content creation, with applications in virtual avatars, AI-driven filmmaking, interactive game engines, and world mo
tl;dr: Are there other models I should try? Is there something better than Kimi K3 for a code review? Is GLM-5.3 better at finding/patching security vulnerabilities? Are skills like https://github.com
arXiv:2610.12737v1 Announce Type: new Abstract: Gradient-based attribution provides input-specific information about model parameters, but computing it via backpropagation for many samples is expensiv
arXiv:2610.13409v1 Announce Type: new Abstract: End-to-end planning in complex scenes must model diverse driving intentions and select the most appropriate trajectory using scene context and future ri